# Data manipulation and plotting modules
import numpy as np
import pandas as pd
from collections import Counter

# Data pre-processing
# z = (x-mean)/stdev
from sklearn.preprocessing import StandardScaler as ss

# Dimensionality reduction
from sklearn.decomposition import PCA

# Data splitting and model parameter search
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV

# Modeling modules
from xgboost.sklearn import XGBClassifier

# Model pipelining
from sklearn.pipeline import Pipeline
from sklearn.pipeline import make_pipeline

# Model evaluation metrics
from sklearn.metrics import accuracy_score, f1_score
from sklearn.metrics import plot_roc_curve
from sklearn.metrics import confusion_matrix

# Visualization
import matplotlib.pyplot as plt
import seaborn as sns
import plotly
import plotly.offline as pyo
import plotly.express as px
import plotly.graph_objs as go
pyo.init_notebook_mode()

import plotly.figure_factory as ff
from xgboost import plot_importance

# Needed for Bayes optimization
# Takes an estimator, performs cross-validation and gives out average scorefrom sklearn.model_selection import cross_val_score

# Misc
import time
import os
import gc
import random
# Used in Randomized parameter search
from scipy.stats import uniform

data = pd.read_csv("C:\\Users\\Lenovo\\Desktop\\Loan Details.csv")

data.shape    

(67463, 35)

data.head()

	ID	Loan Amount	Funded Amount	Funded Amount Investor	Term	Batch Enrolled	Interest Rate	Grade	Sub Grade	Employment Duration	...	Recoveries	Collection Recovery Fee	Collection 12 months Medical	Application Type	Last week Pay	Accounts Delinquent	Total Collection Amount	Total Current Balance	Total Revolving Credit Limit	Loan Status
0	65087372	10000	32236	12329.36286	59	BAT2522922	11.135007	B	C4	MORTGAGE	...	2.498291	0.793724	0	INDIVIDUAL	49	0	31	311301	6619	0
1	1450153	3609	11940	12191.99692	59	BAT1586599	12.237563	C	D3	RENT	...	2.377215	0.974821	0	INDIVIDUAL	109	0	53	182610	20885	0
2	1969101	28276	9311	21603.22455	59	BAT2136391	12.545884	F	D4	MORTGAGE	...	4.316277	1.020075	0	INDIVIDUAL	66	0	34	89801	26155	0
3	6651430	11170	6954	17877.15585	59	BAT2428731	16.731201	C	C3	MORTGAGE	...	0.107020	0.749971	0	INDIVIDUAL	39	0	40	9189	60214	0
4	14354669	16890	13226	13539.92667	59	BAT5341619	15.008300	C	D4	MORTGAGE	...	1294.818751	0.368953	0	INDIVIDUAL	18	0	430	126029	22579	0

5 rows × 35 columns

data.nunique()

ID                              67463
Loan Amount                     27525
Funded Amount                   24548
Funded Amount Investor          67441
Term                                3
Batch Enrolled                     41
Interest Rate                   67448
Grade                               7
Sub Grade                          35
Employment Duration                 3
Home Ownership                  67454
Verification Status                 3
Payment Plan                        1
Loan Title                        109
Debit to Income                 67454
Delinquency - two years             9
Inquires - six months               6
Open Account                       36
Public Record                       5
Revolving Balance               20582
Revolving Utilities             67458
Total Accounts                     69
Initial List Status                 2
Total Received Interest         67451
Total Received Late Fee         67380
Recoveries                      67387
Collection Recovery Fee         67313
Collection 12 months Medical        2
Application Type                    2
Last week Pay                     162
Accounts Delinquent                 1
Total Collection Amount          2193
Total Current Balance           60901
Total Revolving Credit Limit    37708
Loan Status                         2
dtype: int64

data.isnull().sum()

ID                              0
Loan Amount                     0
Funded Amount                   0
Funded Amount Investor          0
Term                            0
Batch Enrolled                  0
Interest Rate                   0
Grade                           0
Sub Grade                       0
Employment Duration             0
Home Ownership                  0
Verification Status             0
Payment Plan                    0
Loan Title                      0
Debit to Income                 0
Delinquency - two years         0
Inquires - six months           0
Open Account                    0
Public Record                   0
Revolving Balance               0
Revolving Utilities             0
Total Accounts                  0
Initial List Status             0
Total Received Interest         0
Total Received Late Fee         0
Recoveries                      0
Collection Recovery Fee         0
Collection 12 months Medical    0
Application Type                0
Last week Pay                   0
Accounts Delinquent             0
Total Collection Amount         0
Total Current Balance           0
Total Revolving Credit Limit    0
Loan Status                     0
dtype: int64

data.describe()

	ID	Loan Amount	Funded Amount	Funded Amount Investor	Term	Interest Rate	Home Ownership	Debit to Income	Delinquency - two years	Inquires - six months	...	Total Received Late Fee	Recoveries	Collection Recovery Fee	Collection 12 months Medical	Last week Pay	Accounts Delinquent	Total Collection Amount	Total Current Balance	Total Revolving Credit Limit	Loan Status
count	6.746300e+04	67463.000000	67463.000000	67463.000000	67463.000000	67463.000000	67463.000000	67463.000000	67463.000000	67463.000000	...	67463.000000	67463.000000	67463.000000	67463.000000	67463.000000	67463.0	67463.000000	6.746300e+04	67463.000000	67463.000000
mean	2.562761e+07	16848.902776	15770.599114	14621.799323	58.173814	11.846258	80541.502522	23.299241	0.327127	0.145754	...	1.143969	59.691578	1.125141	0.021301	71.163260	0.0	146.467990	1.595739e+05	23123.005544	0.092510
std	2.109155e+07	8367.865726	8150.992662	6785.345170	3.327441	3.718629	45029.120366	8.451824	0.800888	0.473291	...	5.244365	357.026346	3.489885	0.144385	43.315845	0.0	744.382233	1.390332e+05	20916.699999	0.289747
min	1.297933e+06	1014.000000	1014.000000	1114.590204	36.000000	5.320006	14573.537170	0.675299	0.000000	0.000000	...	0.000003	0.000036	0.000036	0.000000	0.000000	0.0	1.000000	6.170000e+02	1000.000000	0.000000
25%	6.570288e+06	10012.000000	9266.500000	9831.684984	58.000000	9.297147	51689.843335	16.756416	0.000000	0.000000	...	0.021114	1.629818	0.476259	0.000000	35.000000	0.0	24.000000	5.037900e+04	8155.500000	0.000000
50%	1.791565e+07	16073.000000	13042.000000	12793.682170	59.000000	11.377696	69335.832680	22.656658	0.000000	0.000000	...	0.043398	3.344524	0.780141	0.000000	68.000000	0.0	36.000000	1.183690e+05	16733.000000	0.000000
75%	4.271521e+07	22106.000000	21793.000000	17807.594120	59.000000	14.193533	94623.322785	30.048400	0.000000	0.000000	...	0.071884	5.453727	1.070566	0.000000	105.000000	0.0	46.000000	2.283750e+05	32146.500000	0.000000
max	7.224578e+07	35000.000000	34999.000000	34999.746430	59.000000	27.182348	406561.536400	39.629862	8.000000	5.000000	...	42.618882	4354.467419	166.833000	1.000000	161.000000	0.0	16421.000000	1.177412e+06	201169.000000	1.000000

8 rows × 26 columns

data.dtypes

ID                                int64
Loan Amount                       int64
Funded Amount                     int64
Funded Amount Investor          float64
Term                              int64
Batch Enrolled                   object
Interest Rate                   float64
Grade                            object
Sub Grade                        object
Employment Duration              object
Home Ownership                  float64
Verification Status              object
Payment Plan                     object
Loan Title                       object
Debit to Income                 float64
Delinquency - two years           int64
Inquires - six months             int64
Open Account                      int64
Public Record                     int64
Revolving Balance                 int64
Revolving Utilities             float64
Total Accounts                    int64
Initial List Status              object
Total Received Interest         float64
Total Received Late Fee         float64
Recoveries                      float64
Collection Recovery Fee         float64
Collection 12 months Medical      int64
Application Type                 object
Last week Pay                     int64
Accounts Delinquent               int64
Total Collection Amount           int64
Total Current Balance             int64
Total Revolving Credit Limit      int64
Loan Status                       int64
dtype: object

import seaborn as sns
ax=sns.countplot(x='Loan Status',data=data);

# Finding Correlation,
corrMatrix=data.corr()
corrMatrix 

	ID	Loan Amount	Funded Amount	Funded Amount Investor	Term	Interest Rate	Home Ownership	Debit to Income	Delinquency - two years	Inquires - six months	...	Total Received Late Fee	Recoveries	Collection Recovery Fee	Collection 12 months Medical	Last week Pay	Accounts Delinquent	Total Collection Amount	Total Current Balance	Total Revolving Credit Limit	Loan Status
ID	1.000000	-0.003480	-0.003302	0.002954	0.003226	0.004258	-0.004390	-0.010578	0.000568	-0.006628	...	0.006674	-0.001435	-0.001802	-0.002900	0.000907	NaN	0.003745	-0.003572	-0.005141	0.000472
Loan Amount	-0.003480	1.000000	-0.000551	0.002831	0.004277	-0.004888	0.016691	0.007959	-0.000469	0.008962	...	-0.000034	-0.001606	-0.002142	-0.002726	-0.002362	NaN	-0.004135	-0.008285	0.002289	-0.004473
Funded Amount	-0.003302	-0.000551	1.000000	0.010227	-0.001503	0.002310	-0.003518	0.002347	0.011313	-0.001587	...	0.001542	0.000462	0.000175	0.001071	-0.003476	NaN	-0.002821	-0.001499	0.006145	0.001364
Funded Amount Investor	0.002954	0.002831	0.010227	1.000000	-0.008943	-0.001917	0.001339	0.000112	0.001925	-0.003073	...	-0.000232	0.000966	-0.007272	0.001814	0.004248	NaN	0.006862	0.003283	0.005669	-0.000091
Term	0.003226	0.004277	-0.001503	-0.008943	1.000000	-0.012688	-0.021813	0.001026	-0.004494	-0.005272	...	-0.000736	-0.003475	-0.001203	-0.003263	0.007035	NaN	0.000358	0.003361	-0.005068	-0.003410
Interest Rate	0.004258	-0.004888	0.002310	-0.001917	-0.012688	1.000000	0.005467	-0.011203	0.004045	0.009172	...	0.003119	0.009348	0.001281	-0.009895	-0.012652	NaN	0.002771	-0.002567	0.016651	0.002900
Home Ownership	-0.004390	0.016691	-0.003518	0.001339	-0.021813	0.005467	1.000000	0.022781	-0.003793	0.005024	...	0.004011	0.004399	-0.003821	-0.002639	-0.004573	NaN	0.006314	0.007117	0.005008	0.003716
Debit to Income	-0.010578	0.007959	0.002347	0.000112	0.001026	-0.011203	0.022781	1.000000	-0.004007	0.002246	...	-0.010224	-0.009693	0.002200	-0.000716	0.008639	NaN	0.001555	-0.011582	-0.007236	-0.003057
Delinquency - two years	0.000568	-0.000469	0.011313	0.001925	-0.004494	0.004045	-0.003793	-0.004007	1.000000	0.014679	...	0.007943	0.017348	0.002707	0.003451	0.001160	NaN	0.005642	0.002602	0.009315	0.009990
Inquires - six months	-0.006628	0.008962	-0.001587	-0.003073	-0.005272	0.009172	0.005024	0.002246	0.014679	1.000000	...	0.008296	0.012487	0.008388	-0.004436	-0.004453	NaN	-0.002210	0.001531	0.004678	0.000578
Open Account	-0.002781	0.009088	0.005755	-0.007850	0.021362	-0.003250	0.009080	0.001100	0.004904	-0.002109	...	0.000828	-0.001216	-0.006832	0.003397	-0.013415	NaN	-0.006176	-0.007470	0.003096	-0.007073
Public Record	0.009830	-0.002542	0.003750	0.005002	-0.002827	0.006979	0.005631	-0.007813	0.006716	0.004087	...	0.016594	0.008905	0.004725	0.008878	0.004281	NaN	0.012928	0.003935	0.012046	0.010590
Revolving Balance	0.004315	-0.001738	-0.004485	-0.009102	-0.002317	0.018999	0.016783	-0.011414	0.009394	0.002457	...	0.004903	0.005056	-0.003939	0.007516	-0.010279	NaN	0.004282	-0.007537	0.023366	-0.001073
Revolving Utilities	0.000910	0.014828	0.004460	-0.003027	-0.010018	0.006089	-0.005556	0.003691	0.002474	0.005150	...	-0.001363	-0.002381	-0.001952	0.010783	0.007961	NaN	0.006067	-0.019785	-0.009818	0.004120
Total Accounts	0.001518	-0.002071	0.008298	0.003191	0.001204	0.006584	0.021452	-0.005683	-0.003085	0.007562	...	0.004910	-0.002171	0.007701	0.002538	0.015405	NaN	0.005593	-0.006937	0.032492	0.000222
Total Received Interest	-0.002938	-0.001887	0.002759	0.001432	0.008663	0.006998	-0.010346	0.006504	-0.004511	0.009556	...	0.002507	-0.000717	0.003921	0.004759	0.002643	NaN	0.001027	0.001374	0.012015	0.001680
Total Received Late Fee	0.006674	-0.000034	0.001542	-0.000232	-0.000736	0.003119	0.004011	-0.010224	0.007943	0.008296	...	1.000000	0.007992	0.004856	0.003590	0.001776	NaN	0.007441	-0.000526	0.014839	0.009365
Recoveries	-0.001435	-0.001606	0.000462	0.000966	-0.003475	0.009348	0.004399	-0.009693	0.017348	0.012487	...	0.007992	1.000000	0.008328	0.007874	-0.001787	NaN	0.004703	-0.000488	-0.000146	-0.000652
Collection Recovery Fee	-0.001802	-0.002142	0.000175	-0.007272	-0.001203	0.001281	-0.003821	0.002200	0.002707	0.008388	...	0.004856	0.008328	1.000000	0.007689	0.001106	NaN	0.001800	0.002851	-0.000208	-0.003828
Collection 12 months Medical	-0.002900	-0.002726	0.001071	0.001814	-0.003263	-0.009895	-0.002639	-0.000716	0.003451	-0.004436	...	0.003590	0.007874	0.007689	1.000000	0.005540	NaN	0.004768	0.001728	-0.001033	-0.000686
Last week Pay	0.000907	-0.002362	-0.003476	0.004248	0.007035	-0.012652	-0.004573	0.008639	0.001160	-0.004453	...	0.001776	-0.001787	0.001106	0.005540	1.000000	NaN	0.001457	0.001147	-0.001583	0.006117
Accounts Delinquent	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Total Collection Amount	0.003745	-0.004135	-0.002821	0.006862	0.000358	0.002771	0.006314	0.001555	0.005642	-0.002210	...	0.007441	0.004703	0.001800	0.004768	0.001457	NaN	1.000000	0.005318	0.006541	0.007892
Total Current Balance	-0.003572	-0.008285	-0.001499	0.003283	0.003361	-0.002567	0.007117	-0.011582	0.002602	0.001531	...	-0.000526	-0.000488	0.002851	0.001728	0.001147	NaN	0.005318	1.000000	0.004865	0.009828
Total Revolving Credit Limit	-0.005141	0.002289	0.006145	0.005669	-0.005068	0.016651	0.005008	-0.007236	0.009315	0.004678	...	0.014839	-0.000146	-0.000208	-0.001033	-0.001583	NaN	0.006541	0.004865	1.000000	0.001454
Loan Status	0.000472	-0.004473	0.001364	-0.000091	-0.003410	0.002900	0.003716	-0.003057	0.009990	0.000578	...	0.009365	-0.000652	-0.003828	-0.000686	0.006117	NaN	0.007892	0.009828	0.001454	1.000000

26 rows × 26 columns

ax= plt.axes()
_=sns.heatmap(data.drop(columns=['Total Current Balance']).corr(),cmap='Blues',cbar=None,ax=ax)
_=ax.set_title('Correlation Heatmap')

plt.figure(figsize=(5,5))
a = sns.barplot(x="Grade",y="Total Collection Amount",data=data)

Feature Engineering

data["Loan_qty"] = pd.qcut(
                               data['Loan Amount'],
                               q = 3,
                               labels= ["low", "medium", "high"]
                               )

data.Loan_qty.value_counts()

low       22489
medium    22488
high      22486
Name: Loan_qty, dtype: int64

data.drop(labels='ID', axis=1, inplace=True)
data.drop(labels='Batch Enrolled', axis=1, inplace=True)
data.drop(['Debit to Income','Application Type'], axis=1, inplace=True)

data.head()

	Loan Amount	Funded Amount	Funded Amount Investor	Term	Interest Rate	Grade	Sub Grade	Employment Duration	Home Ownership	Verification Status	...	Recoveries	Collection Recovery Fee	Collection 12 months Medical	Last week Pay	Accounts Delinquent	Total Collection Amount	Total Current Balance	Total Revolving Credit Limit	Loan Status	Loan_qty
0	10000	32236	12329.36286	59	11.135007	B	C4	MORTGAGE	176346.62670	Not Verified	...	2.498291	0.793724	0	49	0	31	311301	6619	0	low
1	3609	11940	12191.99692	59	12.237563	C	D3	RENT	39833.92100	Source Verified	...	2.377215	0.974821	0	109	0	53	182610	20885	0	low
2	28276	9311	21603.22455	59	12.545884	F	D4	MORTGAGE	91506.69105	Source Verified	...	4.316277	1.020075	0	66	0	34	89801	26155	0	high
3	11170	6954	17877.15585	59	16.731201	C	C3	MORTGAGE	108286.57590	Source Verified	...	0.107020	0.749971	0	39	0	40	9189	60214	0	low
4	16890	13226	13539.92667	59	15.008300	C	D4	MORTGAGE	44234.82545	Source Verified	...	1294.818751	0.368953	0	18	0	430	126029	22579	0	medium

5 rows × 32 columns

# Data pre-processing
# Data splitting and model parameter search
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import train_test_split

# Hyperparameter optimization
from sklearn.model_selection import GridSearchCV

from sklearn import metrics
import numpy as np
import pandas as pd
from time import time
import pprint
import joblib
import warnings
warnings.filterwarnings("ignore")

# Classifiers
from catboost import CatBoostClassifier

# Model selection
from sklearn.model_selection import StratifiedKFold

# Metrics
from sklearn.metrics import roc_auc_score
from sklearn.metrics import make_scorer

# Skopt functions
from skopt import BayesSearchCV
from skopt.callbacks import DeadlineStopper, VerboseCallback, DeltaXStopper
from skopt.space import Real, Categorical, Integer
from time import time

#     z = (x-mean)/stdev
from sklearn.preprocessing import StandardScaler as ss

# Dimensionality reduction and noise removal
from sklearn.decomposition import PCA
from xgboost.sklearn import XGBClassifier

# Model pipelining
from sklearn.pipeline import Pipeline
from sklearn.pipeline import make_pipeline

# Hyperparameter optimization
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV

# For plotting
import matplotlib.pyplot as plt

# Model evaluation metrics
from sklearn.metrics import accuracy_score, f1_score
from sklearn.metrics import plot_roc_curve
from sklearn.metrics import confusion_matrix

# From a cell display outputs from multiple commands:
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"

# To check train data file total columns and rows
print("\n Shape")
data.shape                # 67463, 33)

#To check total columns
print("\n\n Columns")
data.columns.values      
print("\n")

#To check Data types
print("\n\nData types")                        
data.dtypes.value_counts()  
print("\n")
data.head(3)

 Shape

(67463, 32)

 Columns

array(['Loan Amount', 'Funded Amount', 'Funded Amount Investor', 'Term',
       'Interest Rate', 'Grade', 'Sub Grade', 'Employment Duration',
       'Home Ownership', 'Verification Status', 'Payment Plan',
       'Loan Title', 'Delinquency - two years', 'Inquires - six months',
       'Open Account', 'Public Record', 'Revolving Balance',
       'Revolving Utilities', 'Total Accounts', 'Initial List Status',
       'Total Received Interest', 'Total Received Late Fee', 'Recoveries',
       'Collection Recovery Fee', 'Collection 12 months Medical',
       'Last week Pay', 'Accounts Delinquent', 'Total Collection Amount',
       'Total Current Balance', 'Total Revolving Credit Limit',
       'Loan Status', 'Loan_qty'], dtype=object)

Data types

int64       16
float64      8
object       7
category     1
dtype: int64

	Loan Amount	Funded Amount	Funded Amount Investor	Term	Interest Rate	Grade	Sub Grade	Employment Duration	Home Ownership	Verification Status	...	Recoveries	Collection Recovery Fee	Collection 12 months Medical	Last week Pay	Accounts Delinquent	Total Collection Amount	Total Current Balance	Total Revolving Credit Limit	Loan Status	Loan_qty
0	10000	32236	12329.36286	59	11.135007	B	C4	MORTGAGE	176346.62670	Not Verified	...	2.498291	0.793724	0	49	0	31	311301	6619	0	low
1	3609	11940	12191.99692	59	12.237563	C	D3	RENT	39833.92100	Source Verified	...	2.377215	0.974821	0	109	0	53	182610	20885	0	low
2	28276	9311	21603.22455	59	12.545884	F	D4	MORTGAGE	91506.69105	Source Verified	...	4.316277	1.020075	0	66	0	34	89801	26155	0	high

3 rows × 32 columns

cat = ['Grade','Sub Grade','Employment Duration','Verification Status','Payment Plan','Loan Title','Initial List Status','Loan_qty']

# Import label encoder 
from sklearn import preprocessing
label_encoder = preprocessing.LabelEncoder()
data[cat]=data[cat].apply(label_encoder.fit_transform)

data.head()

	Loan Amount	Funded Amount	Funded Amount Investor	Term	Interest Rate	Grade	Sub Grade	Employment Duration	Home Ownership	Verification Status	...	Recoveries	Collection Recovery Fee	Collection 12 months Medical	Last week Pay	Accounts Delinquent	Total Collection Amount	Total Current Balance	Total Revolving Credit Limit	Loan Status	Loan_qty
0	10000	32236	12329.36286	59	11.135007	1	13	0	176346.62670	0	...	2.498291	0.793724	0	49	0	31	311301	6619	0	1
1	3609	11940	12191.99692	59	12.237563	2	17	2	39833.92100	1	...	2.377215	0.974821	0	109	0	53	182610	20885	0	1
2	28276	9311	21603.22455	59	12.545884	5	18	0	91506.69105	1	...	4.316277	1.020075	0	66	0	34	89801	26155	0	0
3	11170	6954	17877.15585	59	16.731201	2	12	0	108286.57590	1	...	0.107020	0.749971	0	39	0	40	9189	60214	0	1
4	16890	13226	13539.92667	59	15.008300	2	18	0	44234.82545	1	...	1294.818751	0.368953	0	18	0	430	126029	22579	0	2

5 rows × 32 columns

PIPELINING

#  Divide data into predictors and target
#     First 10 columns are predictors
X = data.iloc[ :, 0:30]
X.head(2)

# 11st column is target
print("\n\nTarget,y, values")
y = data.iloc[ : , 30]
y.head()

	Loan Amount	Funded Amount	Funded Amount Investor	Term	Interest Rate	Grade	Sub Grade	Employment Duration	Home Ownership	Verification Status	...	Total Received Interest	Total Received Late Fee	Recoveries	Collection Recovery Fee	Collection 12 months Medical	Last week Pay	Accounts Delinquent	Total Collection Amount	Total Current Balance	Total Revolving Credit Limit
0	10000	32236	12329.36286	59	11.135007	1	13	0	176346.6267	0	...	2929.646315	0.102055	2.498291	0.793724	0	49	0	31	311301	6619
1	3609	11940	12191.99692	59	12.237563	2	17	2	39833.9210	1	...	772.769385	0.036181	2.377215	0.974821	0	109	0	53	182610	20885

2 rows × 30 columns

Target,y, values

0    0
1    0
2    0
3    0
4    0
Name: Loan Status, dtype: int64

#  Split dataset into train and validation parts
X_train, X_test, y_train, y_test = train_test_split(X,
                                                    y,
                                                    test_size=0.20,
                                                    shuffle = True,
                                                    stratify = y
                                                    )


X_train.shape        
X_test.shape         
y_train.shape        
y_test.shape

(53970, 30)

(13493, 30)

(53970,)

(13493,)

#Creating pipeline
#Pipe using XGBoost
steps_xg = [('sts', ss() ),
            ('pca', PCA()),
            ('xg',  XGBClassifier(silent = False,
                                  n_jobs=3)        # Specify other parameters here
            )
            ]

# Instantiate Pipeline object
pipe_xg = Pipeline(steps_xg)

# Grid Search code to discover best pipeline parameters
print("\n\n--Which parameters can be tuned?--\n\n")
pipe_xg.get_params()

--Which parameters can be tuned?--

{'memory': None,
 'steps': [('sts', StandardScaler()),
  ('pca', PCA()),
  ('xg',
   XGBClassifier(base_score=None, booster=None, callbacks=None,
                 colsample_bylevel=None, colsample_bynode=None,
                 colsample_bytree=None, early_stopping_rounds=None,
                 enable_categorical=False, eval_metric=None, gamma=None,
                 gpu_id=None, grow_policy=None, importance_type=None,
                 interaction_constraints=None, learning_rate=None, max_bin=None,
                 max_cat_to_onehot=None, max_delta_step=None, max_depth=None,
                 max_leaves=None, min_child_weight=None, missing=nan,
                 monotone_constraints=None, n_estimators=100, n_jobs=3,
                 num_parallel_tree=None, predictor=None, random_state=None,
                 reg_alpha=None, reg_lambda=None, ...))],
 'verbose': False,
 'sts': StandardScaler(),
 'pca': PCA(),
 'xg': XGBClassifier(base_score=None, booster=None, callbacks=None,
               colsample_bylevel=None, colsample_bynode=None,
               colsample_bytree=None, early_stopping_rounds=None,
               enable_categorical=False, eval_metric=None, gamma=None,
               gpu_id=None, grow_policy=None, importance_type=None,
               interaction_constraints=None, learning_rate=None, max_bin=None,
               max_cat_to_onehot=None, max_delta_step=None, max_depth=None,
               max_leaves=None, min_child_weight=None, missing=nan,
               monotone_constraints=None, n_estimators=100, n_jobs=3,
               num_parallel_tree=None, predictor=None, random_state=None,
               reg_alpha=None, reg_lambda=None, ...),
 'sts__copy': True,
 'sts__with_mean': True,
 'sts__with_std': True,
 'pca__copy': True,
 'pca__iterated_power': 'auto',
 'pca__n_components': None,
 'pca__n_oversamples': 10,
 'pca__power_iteration_normalizer': 'auto',
 'pca__random_state': None,
 'pca__svd_solver': 'auto',
 'pca__tol': 0.0,
 'pca__whiten': False,
 'xg__objective': 'binary:logistic',
 'xg__use_label_encoder': False,
 'xg__base_score': None,
 'xg__booster': None,
 'xg__callbacks': None,
 'xg__colsample_bylevel': None,
 'xg__colsample_bynode': None,
 'xg__colsample_bytree': None,
 'xg__early_stopping_rounds': None,
 'xg__enable_categorical': False,
 'xg__eval_metric': None,
 'xg__gamma': None,
 'xg__gpu_id': None,
 'xg__grow_policy': None,
 'xg__importance_type': None,
 'xg__interaction_constraints': None,
 'xg__learning_rate': None,
 'xg__max_bin': None,
 'xg__max_cat_to_onehot': None,
 'xg__max_delta_step': None,
 'xg__max_depth': None,
 'xg__max_leaves': None,
 'xg__min_child_weight': None,
 'xg__missing': nan,
 'xg__monotone_constraints': None,
 'xg__n_estimators': 100,
 'xg__n_jobs': 3,
 'xg__num_parallel_tree': None,
 'xg__predictor': None,
 'xg__random_state': None,
 'xg__reg_alpha': None,
 'xg__reg_lambda': None,
 'xg__sampling_method': None,
 'xg__scale_pos_weight': None,
 'xg__subsample': None,
 'xg__tree_method': None,
 'xg__validate_parameters': None,
 'xg__verbosity': None,
 'xg__silent': False}

# Deifne dictionary
parameters = {'xg__learning_rate':  [0.03, 0.05], # learning rate decides what percentage
                                                  #  of error is to be fitted by
                                                  #   by next boosted tree.
                                                  # See this answer in stackoverflow:
                                                  # https://stats.stackexchange.com/questions/354484/why-does-xgboost-have-a-learning-rate
                                                  # Coefficients of boosted trees decide,
                                                  #  in the overall model or scheme, how much importance
                                                  #   each boosted tree shall have. Values of these
                                                  #    Coefficients are calculated by modeling
                                                  #     algorithm and unlike learning rate are
                                                  #      not hyperparameters. These Coefficients
                                                  #       get adjusted by l1 and l2 parameters
              'xg__n_estimators':   [50,  100],  # Number of boosted trees to fit
                                                  # l1 and l2 specifications will change
                                                  # the values of coeff of boosted trees
                                                  # but not their numbers

              'xg__max_depth':      [10,6],
              'pca__n_components' : [30,5],
              'xg__booster': ['gbtree','gblinear']
              }

#  Define revised dictionary
parameters = {'xg__learning_rate':  [0.03, 0.05], # learning rate decides what percentage
                                                  #  of error is to be fitted by
                                                  #   by next boosted tree.
                                                  # See this answer in stackoverflow:
                                                  # https://stats.stackexchange.com/questions/354484/why-does-xgboost-have-a-learning-rate
                                                  # Coefficients of boosted trees decide,
                                                  #  in the overall model or scheme, how much importance
                                                  #   each boosted tree shall have. Values of these
                                                  #    Coefficients are calculated by modeling
                                                  #     algorithm and unlike learning rate are
                                                  #      not hyperparameters. These Coefficients
                                                  #       get adjusted by l1 and l2 parameters
               'xg__max_depth':      [10,6],
               'pca__n_components' : [30,5],
               }          

#### Instantiating GridSearchCV class
clf = GridSearchCV(pipe_xg,            # pipeline object
                   parameters,         # possible parameters
                   n_jobs = 2,         # USe parallel cpu threads
                   cv =2 ,             # No of folds
                   verbose =1,         # Higher the value, more the verbosity
                   scoring = ['accuracy', 'roc_auc'],  # Metrics for performance
                   refit = 'roc_auc'   # Refitting final model on what parameters?
                                       # Those which maximise auc
                   )

from time import sleep
from time import *             #meaning from time import EVERYTHING
import time

# Start fitting pipeline to data
print("\n\n--Takes time...---\n")
start = time.time()
clf.fit(X_train, y_train)
end = time.time()
print()
(end - start)/60

--Takes time...---

Fitting 2 folds for each of 8 candidates, totalling 16 fits
[22:33:41] WARNING: C:/Users/administrator/workspace/xgboost-win64_release_1.6.0/src/learner.cc:627: 
Parameters: { "silent" } might not be used.

  This could be a false alarm, with some parameters getting used by language bindings but
  then being mistakenly passed down to XGBoost core, or some parameter actually being used
  but getting flagged wrongly here. Please open an issue if you find any such cases.

GridSearchCV(cv=2,
             estimator=Pipeline(steps=[('sts', StandardScaler()),
                                       ('pca', PCA()),
                                       ('xg',
                                        XGBClassifier(base_score=None,
                                                      booster=None,
                                                      callbacks=None,
                                                      colsample_bylevel=None,
                                                      colsample_bynode=None,
                                                      colsample_bytree=None,
                                                      early_stopping_rounds=None,
                                                      enable_categorical=False,
                                                      eval_metric=None,
                                                      gamma=None, gpu_id=None,
                                                      grow_policy=None,
                                                      importance_type=None,
                                                      intera...
                                                      max_leaves=None,
                                                      min_child_weight=None,
                                                      missing=nan,
                                                      monotone_constraints=None,
                                                      n_estimators=100,
                                                      n_jobs=3,
                                                      num_parallel_tree=None,
                                                      predictor=None,
                                                      random_state=None,
                                                      reg_alpha=None,
                                                      reg_lambda=None, ...))]),
             n_jobs=2,
             param_grid={'pca__n_components': [30, 5],
                         'xg__learning_rate': [0.03, 0.05],
                         'xg__max_depth': [10, 6]},
             refit='roc_auc', scoring=['accuracy', 'roc_auc'], verbose=1)

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2.2357513189315794

# Make predictions using the best returned model
y_pred = clf.predict(X_test)

# 7.5 Accuracy score
print("\n\n--Accuracy Score--\n")
accuracy = accuracy_score(y_test, y_pred)
accuracy * 100

--Accuracy Score--

90.7507596531535

# F1 score
print("\n\n--F1 Score ")
f1_score(y_test,y_pred,average ='micro')      


# 7.8 ROC curve and get AUC
print("\n\n--ROC curve--\n")
_=plot_roc_curve(clf, X_test, y_test)

--F1 Score 

0.9075075965315349

--ROC curve--

#  Get feature importances from GridSearchCV best fitted 'xg' model
clf.best_estimator_.named_steps["xg"].feature_importances_.shape


print("\n\n---Feature importances---\n")
clf.best_estimator_.named_steps["xg"].feature_importances_

(30,)

---Feature importances---

array([0.03265371, 0.03472751, 0.0308095 , 0.03223895, 0.03245703,
       0.03357385, 0.03266273, 0.03215893, 0.03352813, 0.03415863,
       0.03453534, 0.03328626, 0.03297805, 0.03232114, 0.03327659,
       0.03340273, 0.03570846, 0.03412129, 0.03340523, 0.03336824,
       0.03346148, 0.03315647, 0.03417563, 0.03429782, 0.03236499,
       0.03339149, 0.03259033, 0.03415657, 0.03439463, 0.0326382 ],
      dtype=float32)

colnames = X.columns.tolist()

#  Create a dataframe of feature importances
#      with names of columns and sorted by feature-imp
imp_values = clf.best_estimator_.named_steps["xg"].feature_importances_

df_imp = pd.DataFrame(
                      data = imp_values,
                      index = colnames,
                      columns = ["imp"]
                      ).sort_values(by = 'imp')


df_imp

	imp
Funded Amount Investor	0.030810
Employment Duration	0.032159
Term	0.032239
Inquires - six months	0.032321
Collection 12 months Medical	0.032365
Interest Rate	0.032457
Accounts Delinquent	0.032590
Total Revolving Credit Limit	0.032638
Loan Amount	0.032654
Sub Grade	0.032663
Delinquency - two years	0.032978
Total Received Late Fee	0.033156
Open Account	0.033277
Loan Title	0.033286
Initial List Status	0.033368
Last week Pay	0.033391
Public Record	0.033403
Total Accounts	0.033405
Total Received Interest	0.033461
Home Ownership	0.033528
Grade	0.033574
Revolving Utilities	0.034121
Total Collection Amount	0.034157
Verification Status	0.034159
Recoveries	0.034176
Collection Recovery Fee	0.034298
Total Current Balance	0.034395
Payment Plan	0.034535
Funded Amount	0.034728
Revolving Balance	0.035708

#  First five columns with least feature importance are:
list(df_imp.index.values[:5])

['Funded Amount Investor',
 'Employment Duration',
 'Term',
 'Inquires - six months',
 'Collection 12 months Medical']

# Let us drop these from X_train and X_test
Xtrain = X_train.drop(columns = list(df_imp.index.values[:5]))
Xtest = X_test.drop(columns = list(df_imp.index.values[:5]))

# Build model again with reduced dataset
clf_dr = GridSearchCV(pipe_xg,            # pipeline object
                      parameters,         # possible parameters
                      n_jobs = 2,         # USe parallel cpu threads
                      cv =3 ,             # No of folds
                      verbose =2,         # Higher the value, more the verbosity
                      scoring = ['accuracy', 'roc_auc'],  # Metrics for performance
                      refit = 'roc_auc'   # Those which maximise auc
                     )

           
start = time.time()
clf_dr.fit(Xtrain, y_train)
end = time.time()
(end - start)/60

Fitting 3 folds for each of 8 candidates, totalling 24 fits
[22:34:56] WARNING: C:/Users/administrator/workspace/xgboost-win64_release_1.6.0/src/learner.cc:627: 
Parameters: { "silent" } might not be used.

  This could be a false alarm, with some parameters getting used by language bindings but
  then being mistakenly passed down to XGBoost core, or some parameter actually being used
  but getting flagged wrongly here. Please open an issue if you find any such cases.

GridSearchCV(cv=3,
             estimator=Pipeline(steps=[('sts', StandardScaler()),
                                       ('pca', PCA()),
                                       ('xg',
                                        XGBClassifier(base_score=None,
                                                      booster=None,
                                                      callbacks=None,
                                                      colsample_bylevel=None,
                                                      colsample_bynode=None,
                                                      colsample_bytree=None,
                                                      early_stopping_rounds=None,
                                                      enable_categorical=False,
                                                      eval_metric=None,
                                                      gamma=None, gpu_id=None,
                                                      grow_policy=None,
                                                      importance_type=None,
                                                      intera...
                                                      max_leaves=None,
                                                      min_child_weight=None,
                                                      missing=nan,
                                                      monotone_constraints=None,
                                                      n_estimators=100,
                                                      n_jobs=3,
                                                      num_parallel_tree=None,
                                                      predictor=None,
                                                      random_state=None,
                                                      reg_alpha=None,
                                                      reg_lambda=None, ...))]),
             n_jobs=2,
             param_grid={'pca__n_components': [30, 5],
                         'xg__learning_rate': [0.03, 0.05],
                         'xg__max_depth': [10, 6]},
             refit='roc_auc', scoring=['accuracy', 'roc_auc'], verbose=2)

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0.8580585439999898

# Make predictions
y_pred_dr = clf_dr.predict(Xtest)

# F1 score
f1_score(y_test,y_pred_dr, average ='micro')      
     
f1_score(y_test,y_pred, average ='micro')

0.9075075965315349

0.9075075965315349

Random Search

import os
import gc
import random

# Used in Randomized parameter search
from scipy.stats import uniform

##################### EE. Randomized Search #################

# Tune parameters using randomized search
# Hyperparameters to tune and their ranges
parameters = {'xg__learning_rate':  uniform(0, 1),
              'xg__n_estimators':   range(50,300),
              'xg__max_depth':      range(3,10),
              'pca__n_components' : range(8,10)}

# Tune parameters using random search
#     Create the object first

rs = RandomizedSearchCV(
                          pipe_xg,
                          param_distributions=parameters,
                          scoring= ['roc_auc', 'accuracy'],
                          n_iter=4,           # Max combination of
                                              # parameter to try. Default = 10
                          verbose = 1,
                          refit = 'roc_auc',
                          n_jobs = 2,          # Use parallel cpu threads
                          cv = 2               # No of folds.
                                              # So n_iter * cv combinations
                        )

start = time.time()
rs.fit(X_train, y_train)
end = time.time()
print()
(end - start)/60

Fitting 2 folds for each of 4 candidates, totalling 8 fits
[22:38:56] WARNING: C:/Users/administrator/workspace/xgboost-win64_release_1.6.0/src/learner.cc:627: 
Parameters: { "silent" } might not be used.

  This could be a false alarm, with some parameters getting used by language bindings but
  then being mistakenly passed down to XGBoost core, or some parameter actually being used
  but getting flagged wrongly here. Please open an issue if you find any such cases.

RandomizedSearchCV(cv=2,
                   estimator=Pipeline(steps=[('sts', StandardScaler()),
                                             ('pca', PCA()),
                                             ('xg',
                                              XGBClassifier(base_score=None,
                                                            booster=None,
                                                            callbacks=None,
                                                            colsample_bylevel=None,
                                                            colsample_bynode=None,
                                                            colsample_bytree=None,
                                                            early_stopping_rounds=None,
                                                            enable_categorical=False,
                                                            eval_metric=None,
                                                            gamma=None,
                                                            gpu_id=None,
                                                            grow_policy=None,
                                                            importance_type=None,...
                                                            predictor=None,
                                                            random_state=None,
                                                            reg_alpha=None,
                                                            reg_lambda=None, ...))]),
                   n_iter=4, n_jobs=2,
                   param_distributions={'pca__n_components': range(8, 10),
                                        'xg__learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000001F04419DA90>,
                                        'xg__max_depth': range(3, 10),
                                        'xg__n_estimators': range(50, 300)},
                   refit='roc_auc', scoring=['roc_auc', 'accuracy'], verbose=1)

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0.701155138015747

# Evaluate
f"Best score: {rs.best_score_} " ;print()           
f"Best parameter set: {rs.best_params_} " ; print()


#  Make predictions from the best returned model
y_pred = rs.predict(X_test)


# Accuracy and f1_score
accuracy = accuracy_score(y_test, y_pred)
f"Accuracy: {accuracy * 100.0}"   ; print()      
f"f1 score: {f1_score(y_test,y_pred,average ='micro') }" ; print()

'Best score: 0.501027006832063 '

"Best parameter set: {'pca__n_components': 9, 'xg__learning_rate': 0.711485982806732, 'xg__max_depth': 8, 'xg__n_estimators': 121} "

'Accuracy: 89.42414585340546'

'f1 score: 0.8942414585340547'

Cross Validation

# Cross-validation is a more reliable validation technique than just one train/test split. 
# Here we'll resort to ShuffleSplit to create 5 70%/30% splits
from sklearn.model_selection import ShuffleSplit, KFold
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=17)

from sklearn.model_selection import cross_val_score

from sklearn.ensemble import RandomForestClassifier

from sklearn.ensemble import RandomForestClassifier
#We'll train 2 versions of the RandomForestClassifier model - first with default capacity (trees are not limited in depth), 
#second - with min_samples_leaf=3, i.e. each leave is obliged to have at least 3 instances.
#%%time
model_rf1 = RandomForestClassifier(n_estimators=100, n_jobs=4,
                                   max_depth=None, random_state=17)

# calcuate ROC-AUC for each split
cv_scores_rf1 = cross_val_score(model_rf1, X, y, cv=cv, scoring='roc_auc')

model_rf2 = RandomForestClassifier(n_estimators=100, n_jobs=4,
                                   min_samples_leaf=3, random_state=17)

cv_scores_rf2 = cross_val_score(model_rf2, X, y, cv=cv, 
                                scoring='roc_auc', n_jobs=-1)

#The result returned by cross_val_score is an array with metric values (ROC-AUC) for each split:
cv_scores_rf1

array([0.51963069, 0.50549526, 0.51712541, 0.52198077, 0.51389309])

cv_scores_rf2

array([0.52748311, 0.51399317, 0.52109925, 0.51319326, 0.51747239])

#compare average ROC-AUC among all splits for both models.
print('Model 1 mean score:', cv_scores_rf1.mean())
print('Model 2 mean score:', cv_scores_rf2.mean())

Model 1 mean score: 0.5156250436555302
Model 2 mean score: 0.5186482383475146

Structure

from sklearn.preprocessing import StandardScaler 
from sklearn.preprocessing import LabelEncoder

df = pd.read_csv("C:\\Users\\Lenovo\\Desktop\\Loan Details.csv")

#  Columns in num_data that are either discrete (with few levels)
#     or numeric
cols=['Loan Amount','Funded Amount','Total Accounts','Total Collection Amount','Total Current Balance','Total Revolving Credit Limit','Loan Status','Funded Amount Investor','Interest Rate','Recoveries','Collection Recovery Fee','Accounts Delinquent']

# Create an instance of StandardScaler object
ss = StandardScaler()
le= LabelEncoder()

# Create copy of DataFrame
df['Loan Status']= le.fit_transform(df['Loan Status'])               # Transform boolean to integer

df

	ID	Loan Amount	Funded Amount	Funded Amount Investor	Term	Batch Enrolled	Interest Rate	Grade	Sub Grade	Employment Duration	...	Recoveries	Collection Recovery Fee	Collection 12 months Medical	Application Type	Last week Pay	Accounts Delinquent	Total Collection Amount	Total Current Balance	Total Revolving Credit Limit	Loan Status
0	65087372	10000	32236	12329.36286	59	BAT2522922	11.135007	B	C4	MORTGAGE	...	2.498291	0.793724	0	INDIVIDUAL	49	0	31	311301	6619	0
1	1450153	3609	11940	12191.99692	59	BAT1586599	12.237563	C	D3	RENT	...	2.377215	0.974821	0	INDIVIDUAL	109	0	53	182610	20885	0
2	1969101	28276	9311	21603.22455	59	BAT2136391	12.545884	F	D4	MORTGAGE	...	4.316277	1.020075	0	INDIVIDUAL	66	0	34	89801	26155	0
3	6651430	11170	6954	17877.15585	59	BAT2428731	16.731201	C	C3	MORTGAGE	...	0.107020	0.749971	0	INDIVIDUAL	39	0	40	9189	60214	0
4	14354669	16890	13226	13539.92667	59	BAT5341619	15.008300	C	D4	MORTGAGE	...	1294.818751	0.368953	0	INDIVIDUAL	18	0	430	126029	22579	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
67458	16164945	13601	6848	13175.28583	59	BAT3193689	9.408858	C	A4	MORTGAGE	...	564.614852	0.865230	0	INDIVIDUAL	69	0	48	181775	34301	1
67459	35182714	8323	11046	15637.46301	59	BAT1780517	9.972104	C	B3	RENT	...	2.015494	1.403368	0	INDIVIDUAL	14	0	37	22692	8714	0
67460	16435904	15897	32921	12329.45775	59	BAT1761981	19.650943	A	F3	MORTGAGE	...	5.673092	1.607093	0	INDIVIDUAL	137	0	17	176857	42330	0
67461	5300325	16567	4975	21353.68465	59	BAT2333412	13.169095	D	E3	OWN	...	1.157454	0.207608	0	INDIVIDUAL	73	0	61	361339	39075	0
67462	65443173	15353	29875	14207.44860	59	BAT1930365	16.034631	B	D1	MORTGAGE	...	1.856480	0.366386	0	INDIVIDUAL	54	0	47	196960	66060	0

67463 rows × 35 columns

#  Use fit and transform method
nc = ss.fit_transform(data.loc[:,cols])

# Transform numpy array back to pandas dataframe
#        as we will be using pandas plotting functions
nc = pd.DataFrame(nc, columns = cols)
nc.head(2)

	Loan Amount	Funded Amount	Total Accounts	Total Collection Amount	Total Current Balance	Total Revolving Credit Limit	Loan Status	Funded Amount Investor	Interest Rate	Recoveries	Collection Recovery Fee	Accounts Delinquent
0	-0.818483	2.020064	-1.397725	-0.155120	1.091309	-0.789041	-0.319281	-0.337854	-0.191268	-0.160195	-0.094966	0.0
1	-1.582243	-0.469958	-0.676500	-0.125565	0.165689	-0.106997	-0.319281	-0.358098	0.105229	-0.160534	-0.043073	0.0

#  Add/overwrite few columns that are discrete
#        These columns were not to be scaled
nc['Loan Status'] = data['Loan Status']
nc['Accounts Delinquent'] = data['Accounts Delinquent']

# Parallel coordinates chart
fig1 = plt.figure()
pd.plotting.parallel_coordinates(nc,
                                 'Loan Status',    # class_column
                                  colormap='winter'
                                  )
plt.xticks(rotation=90)
plt.title("Parallel chart with data")

<AxesSubplot:>

(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10]),
 [Text(0, 0, 'Loan Amount'),
  Text(1, 0, 'Funded Amount'),
  Text(2, 0, 'Total Accounts'),
  Text(3, 0, 'Total Collection Amount'),
  Text(4, 0, 'Total Current Balance'),
  Text(5, 0, 'Total Revolving Credit Limit'),
  Text(6, 0, 'Funded Amount Investor'),
  Text(7, 0, 'Interest Rate'),
  Text(8, 0, 'Recoveries'),
  Text(9, 0, 'Collection Recovery Fee'),
  Text(10, 0, 'Accounts Delinquent')])

Text(0.5, 1.0, 'Parallel chart with data')

# Andrews charts
fig3 = plt.figure()
pd.plotting.andrews_curves(nc,
                           'Loan Status',
                           colormap = 'winter')

plt.title("Andrews plots with  data")

<AxesSubplot:>

Text(0.5, 1.0, 'Andrews plots with  data')

#  Radviz plot
fig5 = plt.figure()
pd.plotting.radviz(nc,
                   class_column ='Loan Status',
                   colormap= plt.cm.winter,
                   alpha = 0.4
                   )

<AxesSubplot:>